Fix seed so that data is properly shuffled between epochs

def train(args, epoch, batch_offset, trainer, dataset, max_positions, num_gpus):

    """Train the model for one epoch."""

    torch.manual_seed(args.seed + epoch)

    trainer.set_seed(args.seed + epoch)

    seed = args.seed + epoch

    torch.manual_seed(seed)

    trainer.set_seed(seed)

    itr = dataset.dataloader(

        args.train_subset, num_workers=args.workers, max_tokens=args.max_tokens,

        seed=args.seed, epoch=epoch, max_positions=max_positions,

        seed=seed, epoch=epoch, max_positions=max_positions,

        sample_without_replacement=args.sample_without_replacement,

        skip_invalid_size_inputs_valid_test=args.skip_invalid_size_inputs_valid_test,

        sort_by_source_size=(epoch <= args.curriculum))